Hypotheses

Check all hypotheses between traits and environmental drivers, and among traits

Big PCA

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, herbivores)
SLA	++	D	disturbance favours fast-growing species	NA	more nutrients allow for ‘cheap’ leaves	NA	0.35	0.2 - 0.5	0.0000671	✅	0.48	0.12	0.01	0.35	0.2 - 0.5	✅
Seed_mass	–	D	disturbance selects for smaller seeds (= colonisation)	Diaz et al. 2016	NA	NA	-0.29	-0.45 - -0.14	0.0008729	✅	-0.37	-0.35	0.04	-0.29	-0.45 - -0.14	✅
LDMC	–	D	Tougher = slow	NA	NA	NA	-0.36	-0.51 - -0.21	0.0000000	✅	-0.37	-0.52	0.01	-0.36	-0.51 - -0.21	✅
LeafN	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	Diaz et al. 2016	0.48	0.34 - 0.62	0.0000000	✅	0.47	0.56	0.03	0.48	0.34 - 0.62	✅
Arthropods (above, omnicarnivores)
LeafP	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	NA	0.53	0.39 - 0.66	0.0000000	✅	0.41	0.53	0.14	0.53	0.39 - 0.66	✅
Root_tissue_density	–	D	NA	NA	conservative and/or ‘collaboration’ => if few nutrients, need for collaboration	Bergmann et al. 2020	-0.27	-0.42 - -0.11	0.0024022	✅	-0.38	-0.12	0.02	-0.27	-0.42 - -0.11	✅
Arthropods (below, herbivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.08	-0.08 - 0.25	0.4014453	Inconclusive	0.11	-0.27	0.08	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.32	0.17 - 0.48	0.0003525	✅	0.67	0.34	-0.27	0.32	0.17 - 0.48	✅
Ah_BodySize	+/-	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.26	-0.42 - -0.1	0.0030785	B	-0.26	-0.28	-0.03	-0.26	-0.42 - -0.1	B
Arthropods (below, predators)
Ah_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.28	0.13 - 0.44	0.0012847	✅	0.32	0.45	-0.04	0.28	0.13 - 0.44	✅
Ah_Generalism	++	NA	NA	NA	NA	NA	0.39	0.24 - 0.54	0.0000000	✅	0.14	0.41	0.21	0.39	0.24 - 0.54	✅
Bats
Ah_Generations	++	I	NA	NA	NA	NA	0.54	0.4 - 0.68	0.0000000	✅	0.81	0.47	-0.12	0.54	0.4 - 0.68	✅
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.01	-0.17 - 0.15	0.9357700	Inconclusive	0.13	0.02	-0.08	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.11	-0.05 - 0.27	0.2588871	Inconclusive	-0.04	-0.05	0.18	0.11	-0.05 - 0.27	Inconclusive
Birds (insectivorous)
Ah_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.28	-0.46 - -0.1	0.0062122	✅	-0.43	-0.44	0.22	-0.25	-0.41 - -0.09	✅
Ah_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.16	-0.02 - 0.35	0.1305387	Inconclusive	0.04	0.14	0.12	0.15	-0.01 - 0.32	Inconclusive
Ah_b_Generalism	++	NA	NA	NA	NA	NA	-0.09	-0.28 - 0.1	0.4416857	Inconclusive	0.13	0.03	-0.28	-0.09	-0.28 - 0.1	Inconclusive
Bi_Size	++	NA	more = fast	NA	NA	NA	0.31	0.16 - 0.47	0.0005640	✅	0.30	0.17	0.10	0.31	0.16 - 0.47	✅
Bi_Incub	++	NA	more = disturbance	NA	NA	NA	0.18	0.02 - 0.34	0.0518988	✔	0.32	0.07	-0.03	0.18	0.02 - 0.34	✔
Butterflies
Bi_TOffsprings	–	I	Small = fast	NA	NA	NA	-0.15	-0.31 - 0.01	0.1079747	Inconclusive	-0.38	-0.06	0.10	-0.15	-0.31 - 0.01	Inconclusive
Bi_GenLength	–	NA	NA	NA	NA	NA	0.27	0.11 - 0.42	0.0029932	❌	0.32	0.17	0.03	0.27	0.11 - 0.42	❌
Bi_AgeMax	–	I	Small = fast	NA	NA	NA	0.30	0.15 - 0.46	0.0007231	❌	0.23	0.20	0.12	0.30	0.15 - 0.46	❌
but_flight	++	NA	Early emergence favorable bc disturbance starts early	Börschig et al. 2013	NA	NA	0.25	0.09 - 0.41	0.0047448	✅	0.77	0.36	-0.37	0.25	0.09 - 0.41	✅
but_GenYear	++	NA	High reproduction can compensate mortality due to disturbance	Börschig et al. 2013	NA	NA	0.23	0.07 - 0.39	0.0104904	✅	0.11	0.07	0.15	0.23	0.07 - 0.39	✅
Collembola
but_hibernation	++	NA	Later hibernation stage means butterflies ready before disturbance	NA	NA	NA	0.24	0.08 - 0.4	0.0062122	✅	0.73	0.30	-0.33	0.25	0.09 - 0.41	✅
but_Size	++	NA	Larger wings = more dispersal	Börschig et al. 2013	NA	NA	-0.12	-0.28 - 0.05	0.2292461	Inconclusive	0.06	0.07	-0.21	-0.13	-0.29 - 0.03	Inconclusive
but_Generalism	++	NA	NA	NA	High nutrients = low plant diversity, hence more generalists	NA	0.28	0.12 - 0.44	0.0018800	✅	0.12	0.03	0.22	0.28	0.12 - 0.44	✅
col_Sex	–	NA	NA	NA	NA	NA	0.01	-0.16 - 0.18	0.9411600	Inconclusive	0.03	0.08	-0.04	0.01	-0.16 - 0.17	Inconclusive
Microbes
col_Gen_per_Year	++	NA	Furca = escape disturbance	NA	NA	NA	-0.09	-0.26 - 0.08	0.3999297	Inconclusive	0.26	-0.01	-0.30	-0.09	-0.26 - 0.08	Inconclusive
col_Depth	++	NA	NA	NA	NA	NA	-0.01	-0.18 - 0.16	0.9285170	Inconclusive	-0.07	-0.09	0.06	-0.01	-0.18 - 0.16	Inconclusive
col_Size	–	NA	a/ more resources or b/ selected	NA	NA	NA	0.04	-0.13 - 0.21	0.7161345	Inconclusive	-0.23	-0.09	0.24	0.04	-0.13 - 0.21	Inconclusive
mites_DaysAdult	–	NA	longer lifespan = slow	NA	NA	NA	-0.05	-0.22 - 0.12	0.6927679	Inconclusive	0.08	-0.14	-0.06	-0.05	-0.22 - 0.12	Inconclusive
mites_Mass	–	NA	Mass = size = slow	NA	NA	NA	-0.16	-0.32 - 0.01	0.1046641	Inconclusive	0.19	-0.12	-0.30	-0.16	-0.32 - 0.01	Inconclusive
Mites
mites_Sex	–	I	Habitat openness = sexual repro, more LUI = more growth	NA	NA	NA	0.01	-0.16 - 0.18	0.9351016	Inconclusive	0.28	0.01	-0.21	0.01	-0.16 - 0.18	Inconclusive
mites_Hab_spec	++	NA	more disturbance = more in the soil	NA	NA	NA	-0.03	-0.2 - 0.14	0.8138542	Inconclusive	-0.05	-0.10	0.02	-0.03	-0.2 - 0.14	Inconclusive
mites_Feed_spec	++	NA	NA	NA	NA	NA	0.12	-0.04 - 0.29	0.2139969	Inconclusive	0.23	0.07	-0.06	0.12	-0.04 - 0.29	Inconclusive
P_patho	++	NA	More bacteria = more bacterivores	NA	NA	NA	0.46	0.32 - 0.61	0.0000000	✅	0.45	0.40	0.08	0.46	0.32 - 0.61	✅
Pb_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.27	-0.43 - -0.11	0.0022118	✅	-0.24	-0.09	-0.14	-0.27	-0.43 - -0.11	✅
Plants (AG)
Ps_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.31	-0.46 - -0.15	0.0005875	✅	-0.06	-0.15	-0.28	-0.31	-0.46 - -0.15	✅
mic_FB	–	D and I	Fungi slower than bacteria, linked to slow plant traits	NA	Fungi dominate in low-nutrient soils; associated with slow plants	de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019	-0.24	-0.4 - -0.08	0.0062863	✅	-0.47	-0.43	0.20	-0.24	-0.4 - -0.08	✅
mic_Fpathotroph	++	NA	Fast= more parasites	check https://www.nature.com/articles/s41467-021-23605-y	Fast plant invest less in defenses, so more pathogens	NA	0.31	0.15 - 0.46	0.0005640	✅	0.21	0.51	0.05	0.31	0.15 - 0.46	✅
mic_O.C.ratio	–	NA	NA	NA	more oligo (Acido) than copio (Actibo, alphapto…) in slow	Check Neff 2015, Ramirez 2010, Fierer 2012	-0.04	-0.2 - 0.12	0.6947517	Inconclusive	0.07	-0.12	-0.03	-0.04	-0.2 - 0.12	Inconclusive
mic_Bvolume	+/-	D	a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations	Westoby et al. 2021	NA	NA	-0.31	-0.46 - -0.15	0.0005875	B	-0.09	-0.10	-0.25	-0.31	-0.46 - -0.15	B
Plants (BG)
mic_Bgenome_size	–	D	NA	NA	Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)	Leff et al. 2015; Konstantinidis (2004)	-0.04	-0.21 - 0.12	0.6947517	Inconclusive	-0.27	-0.48	0.33	-0.04	-0.21 - 0.12	Inconclusive
Protists
bat_mass	–	NA	Large more sensitive to disturbance BUT large less sensitive to urbanisation??	Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018	NA	NA	-0.17	-0.33 - -0.01	0.0624926	✔	0.03	0.14	-0.29	-0.17	-0.33 - -0.01	✔
Protists bacterivores
bat_lifespan	NA	NA	NA	NA	NA	NA	0.06	-0.1 - 0.23	0.5392508	Inconclusive	-0.18	-0.30	0.30	0.06	-0.1 - 0.23	Inconclusive
Protists predators
bat_offspring	++	NA	opposite to size	NA	NA	NA	-0.16	-0.32 - 0.01	0.0983643	Inconclusive	0.15	0.27	-0.39	-0.16	-0.32 - 0.01	Inconclusive

Identification of strategy axes for each group

Plants, above- and below-ground

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Plants (AG)
SLA	++	D	disturbance favours fast-growing species	NA	more nutrients allow for ‘cheap’ leaves	NA	0.35	0.2 - 0.5	0.0000671	✅	0.48	0.12	0.01	0.35	0.2 - 0.5	✅
Seed_mass	–	D	disturbance selects for smaller seeds (= colonisation)	Diaz et al. 2016	NA	NA	-0.29	-0.45 - -0.14	0.0008729	✅	-0.37	-0.35	0.04	-0.29	-0.45 - -0.14	✅
LDMC	–	D	Tougher = slow	NA	NA	NA	-0.36	-0.51 - -0.21	0.0000000	✅	-0.37	-0.52	0.01	-0.36	-0.51 - -0.21	✅
LeafN	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	Diaz et al. 2016	0.48	0.34 - 0.62	0.0000000	✅	0.47	0.56	0.03	0.48	0.34 - 0.62	✅
LeafP	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	NA	0.53	0.39 - 0.66	0.0000000	✅	0.41	0.53	0.14	0.53	0.39 - 0.66	✅
Plants (BG)
Root_tissue_density	–	D	NA	NA	conservative and/or ‘collaboration’ => if few nutrients, need for collaboration	Bergmann et al. 2020	-0.27	-0.42 - -0.11	0.0024022	✅	-0.38	-0.12	0.02	-0.27	-0.42 - -0.11	✅

## [1] "Plants, All"

## Registered S3 methods overwritten by 'car':
##   method                          from
##   influence.merMod                lme4
##   cooks.distance.influence.merMod lme4
##   dfbeta.influence.merMod         lme4
##   dfbetas.influence.merMod        lme4

Bacteria & fungi

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Microbes
mic_FB	–	D and I	Fungi slower than bacteria, linked to slow plant traits	NA	Fungi dominate in low-nutrient soils; associated with slow plants	de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019	-0.24	-0.4 - -0.08	0.0062863	✅	-0.47	-0.43	0.20	-0.24	-0.4 - -0.08	✅
mic_Fpathotroph	++	NA	Fast= more parasites	check https://www.nature.com/articles/s41467-021-23605-y	Fast plant invest less in defenses, so more pathogens	NA	0.31	0.15 - 0.46	0.0005640	✅	0.21	0.51	0.05	0.31	0.15 - 0.46	✅
mic_O.C.ratio	–	NA	NA	NA	more oligo (Acido) than copio (Actibo, alphapto…) in slow	Check Neff 2015, Ramirez 2010, Fierer 2012	-0.04	-0.2 - 0.12	0.6947517	Inconclusive	0.07	-0.12	-0.03	-0.04	-0.2 - 0.12	Inconclusive
mic_Bvolume	+/-	D	a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations	Westoby et al. 2021	NA	NA	-0.31	-0.46 - -0.15	0.0005875	B	-0.09	-0.10	-0.25	-0.31	-0.46 - -0.15	B
mic_Bgenome_size	–	D	NA	NA	Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)	Leff et al. 2015; Konstantinidis (2004)	-0.04	-0.21 - 0.12	0.6947517	Inconclusive	-0.27	-0.48	0.33	-0.04	-0.21 - 0.12	Inconclusive

Arthropods, above-ground

Herbivores

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, herbivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.08	-0.08 - 0.25	0.4014453	Inconclusive	0.11	-0.27	0.08	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.32	0.17 - 0.48	0.0003525	✅	0.67	0.34	-0.27	0.32	0.17 - 0.48	✅
Ah_BodySize	+/-	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.26	-0.42 - -0.1	0.0030785	B	-0.26	-0.28	-0.03	-0.26	-0.42 - -0.1	B
Ah_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.28	0.13 - 0.44	0.0012847	✅	0.32	0.45	-0.04	0.28	0.13 - 0.44	✅
Arthropods (above, omnicarnivores)
Ah_Generalism	++	NA	NA	NA	NA	NA	0.39	0.24 - 0.54	0.0000000	✅	0.14	0.41	0.21	0.39	0.24 - 0.54	✅
Ah_Generations	++	I	NA	NA	NA	NA	0.54	0.4 - 0.68	0.0000000	✅	0.81	0.47	-0.12	0.54	0.4 - 0.68	✅
Arthropods (below, herbivores)
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.01	-0.17 - 0.15	0.9357700	Inconclusive	0.13	0.02	-0.08	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.11	-0.05 - 0.27	0.2588871	Inconclusive	-0.04	-0.05	0.18	0.11	-0.05 - 0.27	Inconclusive
Ah_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.28	-0.46 - -0.1	0.0062122	✅	-0.43	-0.44	0.22	-0.25	-0.41 - -0.09	✅
Arthropods (below, predators)
Ah_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.16	-0.02 - 0.35	0.1305387	Inconclusive	0.04	0.14	0.12	0.15	-0.01 - 0.32	Inconclusive
Ah_b_Generalism	++	NA	NA	NA	NA	NA	-0.09	-0.28 - 0.1	0.4416857	Inconclusive	0.13	0.03	-0.28	-0.09	-0.28 - 0.1	Inconclusive

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Butterflies
but_flight	++	NA	Early emergence favorable bc disturbance starts early	Börschig et al. 2013	NA	NA	0.25	0.09 - 0.41	0.0047448	✅	0.77	0.36	-0.37	0.25	0.09 - 0.41	✅
but_GenYear	++	NA	High reproduction can compensate mortality due to disturbance	Börschig et al. 2013	NA	NA	0.23	0.07 - 0.39	0.0104904	✅	0.11	0.07	0.15	0.23	0.07 - 0.39	✅
but_hibernation	++	NA	Later hibernation stage means butterflies ready before disturbance	NA	NA	NA	0.24	0.08 - 0.4	0.0062122	✅	0.73	0.30	-0.33	0.25	0.09 - 0.41	✅
but_Size	++	NA	Larger wings = more dispersal	Börschig et al. 2013	NA	NA	-0.12	-0.28 - 0.05	0.2292461	Inconclusive	0.06	0.07	-0.21	-0.13	-0.29 - 0.03	Inconclusive
but_Generalism	++	NA	NA	NA	High nutrients = low plant diversity, hence more generalists	NA	0.28	0.12 - 0.44	0.0018800	✅	0.12	0.03	0.22	0.28	0.12 - 0.44	✅

Predators

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, omnicarnivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.08	-0.08 - 0.25	0.4014453	Inconclusive	0.11	-0.27	0.08	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.32	0.17 - 0.48	0.0003525	✅	0.67	0.34	-0.27	0.32	0.17 - 0.48	✅
Arthropods (below, predators)
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.01	-0.17 - 0.15	0.9357700	Inconclusive	0.13	0.02	-0.08	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.11	-0.05 - 0.27	0.2588871	Inconclusive	-0.04	-0.05	0.18	0.11	-0.05 - 0.27	Inconclusive

Protists

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Protists
P_patho	++	NA	More bacteria = more bacterivores	NA	NA	NA	0.46	0.32 - 0.61	0.0000000	✅	0.45	0.40	0.08	0.46	0.32 - 0.61	✅
Protists bacterivores
Pb_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.27	-0.43 - -0.11	0.0022118	✅	-0.24	-0.09	-0.14	-0.27	-0.43 - -0.11	✅
Protists predators
Ps_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.31	-0.46 - -0.15	0.0005875	✅	-0.06	-0.15	-0.28	-0.31	-0.46 - -0.15	✅

Birds

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Birds (insectivorous)
Bi_Size	++	NA	more = fast	NA	NA	NA	0.31	0.16 - 0.47	0.0005640	✅	0.30	0.17	0.10	0.31	0.16 - 0.47	✅
Bi_Incub	++	NA	more = disturbance	NA	NA	NA	0.18	0.02 - 0.34	0.0518988	✔	0.32	0.07	-0.03	0.18	0.02 - 0.34	✔
Bi_TOffsprings	–	I	Small = fast	NA	NA	NA	-0.15	-0.31 - 0.01	0.1079747	Inconclusive	-0.38	-0.06	0.10	-0.15	-0.31 - 0.01	Inconclusive
Bi_GenLength	–	NA	NA	NA	NA	NA	0.27	0.11 - 0.42	0.0029932	❌	0.32	0.17	0.03	0.27	0.11 - 0.42	❌
Bi_AgeMax	–	I	Small = fast	NA	NA	NA	0.30	0.15 - 0.46	0.0007231	❌	0.23	0.20	0.12	0.30	0.15 - 0.46	❌

Mites & collembola

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Mites
mites_DaysAdult	–	NA	longer lifespan = slow	NA	NA	NA	-0.05	-0.22 - 0.12	0.6927679	Inconclusive	0.08	-0.14	-0.06	-0.05	-0.22 - 0.12	Inconclusive
mites_Mass	–	NA	Mass = size = slow	NA	NA	NA	-0.16	-0.32 - 0.01	0.1046641	Inconclusive	0.19	-0.12	-0.30	-0.16	-0.32 - 0.01	Inconclusive
mites_Sex	–	I	Habitat openness = sexual repro, more LUI = more growth	NA	NA	NA	0.01	-0.16 - 0.18	0.9351016	Inconclusive	0.28	0.01	-0.21	0.01	-0.16 - 0.18	Inconclusive
mites_Hab_spec	++	NA	more disturbance = more in the soil	NA	NA	NA	-0.03	-0.2 - 0.14	0.8138542	Inconclusive	-0.05	-0.10	0.02	-0.03	-0.2 - 0.14	Inconclusive
mites_Feed_spec	++	NA	NA	NA	NA	NA	0.12	-0.04 - 0.29	0.2139969	Inconclusive	0.23	0.07	-0.06	0.12	-0.04 - 0.29	Inconclusive

## NULL

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Collembola
col_Sex	–	NA	NA	NA	NA	NA	0.01	-0.16 - 0.18	0.9411600	Inconclusive	0.03	0.08	-0.04	0.01	-0.16 - 0.17	Inconclusive
col_Gen_per_Year	++	NA	Furca = escape disturbance	NA	NA	NA	-0.09	-0.26 - 0.08	0.3999297	Inconclusive	0.26	-0.01	-0.30	-0.09	-0.26 - 0.08	Inconclusive
col_Depth	++	NA	NA	NA	NA	NA	-0.01	-0.18 - 0.16	0.9285170	Inconclusive	-0.07	-0.09	0.06	-0.01	-0.18 - 0.16	Inconclusive
col_Size	–	NA	a/ more resources or b/ selected	NA	NA	NA	0.04	-0.13 - 0.21	0.7161345	Inconclusive	-0.23	-0.09	0.24	0.04	-0.13 - 0.21	Inconclusive

## NULL

Bats

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Bats
bat_mass	–	NA	Large more sensitive to disturbance BUT large less sensitive to urbanisation??	Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018	NA	NA	-0.17	-0.33 - -0.01	0.0624926	✔	0.03	0.14	-0.29	-0.17	-0.33 - -0.01	✔
bat_lifespan	NA	NA	NA	NA	NA	NA	0.06	-0.1 - 0.23	0.5392508	Inconclusive	-0.18	-0.30	0.30	0.06	-0.1 - 0.23	Inconclusive
bat_offspring	++	NA	opposite to size	NA	NA	NA	-0.16	-0.32 - 0.01	0.0983643	Inconclusive	0.15	0.27	-0.39	-0.16	-0.32 - 0.01	Inconclusive

## NULL

Try to do a SEM

## 
##  Pearson's product-moment correlation
## 
## data:  PCA_pca$ind$coord[, 1] and env_data_lui[Plot %in% dd$Plot, ]$LUI
## t = 6.5034, df = 148, p-value = 1.135e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3366118 0.5873277
## sample estimates:
##      cor 
## 0.471441

Run above-ground model, simple

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"

##            cfi          rmsea rmsea.ci.upper            bic 
##          0.979          0.181          0.333       2400.988

## quartz_off_screen 
##                 2

Run below-ground model, simple

## lavaan 0.6-9 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        40
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                12.932
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.012
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.568    0.077    7.368    0.000    0.568    0.558
##   Protists_patho ~                                                            
##     LUI                     0.291    0.092    3.152    0.002    0.291    0.285
##     plant                   0.364    0.091    4.008    0.000    0.364    0.362
##   Microbes ~                                                                  
##     LUI                    -0.066    0.087   -0.757    0.449   -0.066   -0.069
##     plant                   0.571    0.086    6.650    0.000    0.571    0.603
##   Protists_bact ~                                                             
##     LUI                     0.270    0.103    2.620    0.009    0.270    0.286
##     plant                   0.049    0.119    0.410    0.682    0.049    0.053
##     Microbes               -0.158    0.103   -1.533    0.125   -0.158   -0.162
##     Protists_patho          0.001    0.097    0.011    0.992    0.001    0.001
##   Protists_sec ~                                                              
##     LUI                     0.197    0.103    1.922    0.055    0.197    0.210
##     Protists_bact          -0.056    0.088   -0.639    0.523   -0.056   -0.057
##     plant                  -0.012    0.115   -0.101    0.920   -0.012   -0.013
##     Microbes                0.167    0.101    1.652    0.098    0.167    0.172
##     Protists_patho          0.119    0.094    1.260    0.208    0.119    0.130
##   Mites ~                                                                     
##     LUI                     0.037    0.109    0.341    0.733    0.037    0.039
##     plant                   0.122    0.121    1.004    0.315    0.122    0.129
##     Microbes                0.190    0.107    1.771    0.077    0.190    0.191
##     Protists_sec           -0.031    0.096   -0.325    0.745   -0.031   -0.030
##     Protists_bact           0.124    0.093    1.327    0.184    0.124    0.121
##     Protists_patho         -0.234    0.100   -2.341    0.019   -0.234   -0.249
##   Coll ~                                                                      
##     LUI                     0.043    0.115    0.378    0.705    0.043    0.044
##     Microbes               -0.201    0.113   -1.784    0.074   -0.201   -0.197
##     plant                   0.143    0.127    1.121    0.262    0.143    0.148
##     Protists_sec           -0.064    0.101   -0.634    0.526   -0.064   -0.061
##     Protists_bact          -0.105    0.098   -1.078    0.281   -0.105   -0.101
##     Protists_patho         -0.117    0.105   -1.116    0.264   -0.117   -0.122
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.094    0.102    0.922    0.356    0.094    0.097
##     Mites                   0.353    0.086    4.096    0.000    0.353    0.349
##     Coll                   -0.230    0.082   -2.801    0.005   -0.230   -0.234
##     Protists_sec           -0.022    0.091   -0.240    0.810   -0.022   -0.021
##     plant                  -0.112    0.103   -1.089    0.276   -0.112   -0.118
##     Protists_patho          0.048    0.099    0.489    0.625    0.048    0.051
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.761    0.098    7.746    0.000    0.761    0.689
##    .Protists_patho    0.753    0.097    7.746    0.000    0.753    0.673
##    .Microbes          0.672    0.087    7.746    0.000    0.672    0.678
##    .Protists_bact     0.858    0.111    7.746    0.000    0.858    0.906
##    .Protists_sec      0.804    0.104    7.746    0.000    0.804    0.862
##    .Mites             0.889    0.115    7.746    0.000    0.889    0.905
##    .Coll              0.982    0.127    7.746    0.000    0.982    0.951
##    .Arth_mncrn_blw    0.822    0.106    7.746    0.000    0.822    0.819

## lavaan 0.6-9 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        36
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                19.371
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.013
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.568    0.077    7.368    0.000    0.568    0.558
##   Protists_patho ~                                                            
##     LUI                     0.289    0.092    3.128    0.002    0.289    0.282
##     plant                   0.368    0.091    4.058    0.000    0.368    0.366
##   Microbes ~                                                                  
##     LUI                    -0.066    0.087   -0.757    0.449   -0.066   -0.069
##     plant                   0.571    0.086    6.650    0.000    0.571    0.603
##   Protists_bact ~                                                             
##     LUI                     0.270    0.099    2.729    0.006    0.270    0.287
##     plant                   0.049    0.113    0.431    0.666    0.049    0.053
##     Microbes               -0.158    0.103   -1.530    0.126   -0.158   -0.161
##   Protists_sec ~                                                              
##     LUI                     0.234    0.099    2.356    0.018    0.234    0.249
##     plant                   0.011    0.111    0.098    0.922    0.011    0.012
##     Microbes                0.203    0.101    2.002    0.045    0.203    0.208
##     Protists_bact          -0.056    0.089   -0.634    0.526   -0.056   -0.056
##   Mites ~                                                                     
##     LUI                    -0.030    0.108   -0.277    0.782   -0.030   -0.032
##     plant                   0.078    0.118    0.659    0.510    0.078    0.083
##     Microbes                0.123    0.110    1.117    0.264    0.123    0.124
##     Protists_sec           -0.055    0.097   -0.562    0.574   -0.055   -0.054
##     Protists_bact           0.122    0.095    1.284    0.199    0.122    0.121
##   Coll ~                                                                      
##     LUI                     0.010    0.112    0.087    0.931    0.010    0.010
##     plant                   0.121    0.122    0.989    0.323    0.121    0.124
##     Microbes               -0.234    0.114   -2.061    0.039   -0.234   -0.229
##     Protists_bact          -0.106    0.098   -1.081    0.280   -0.106   -0.101
##     Protists_sec           -0.076    0.101   -0.752    0.452   -0.076   -0.072
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.023    0.087    0.263    0.793    0.023    0.024
##     Mites                   0.332    0.084    3.926    0.000    0.332    0.324
##     Coll                   -0.222    0.081   -2.730    0.006   -0.222   -0.226
##     Protists_sec           -0.022    0.090   -0.243    0.808   -0.022   -0.021
##     Protists_bact           0.127    0.089    1.430    0.153    0.127    0.123
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .Protists_patho ~~                                                      
##    .Arth_mncrn_blw     0.039    0.072    0.539    0.590    0.039    0.049
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.761    0.098    7.746    0.000    0.761    0.689
##    .Protists_patho    0.753    0.097    7.746    0.000    0.753    0.672
##    .Microbes          0.672    0.087    7.746    0.000    0.672    0.678
##    .Protists_bact     0.858    0.111    7.746    0.000    0.858    0.907
##    .Protists_sec      0.814    0.105    7.746    0.000    0.814    0.864
##    .Mites             0.927    0.120    7.746    0.000    0.927    0.959
##    .Coll              0.991    0.128    7.746    0.000    0.991    0.951
##    .Arth_mncrn_blw    0.817    0.105    7.746    0.000    0.817    0.809

##            cfi          rmsea rmsea.ci.upper            bic 
##          0.940          0.109          0.172       2704.408

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"

##            cfi          rmsea rmsea.ci.upper            bic 
##          0.999          0.012          0.097       3262.850

## quartz_off_screen 
##                 2

Use the parameters defined in the simple SEM

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

## The following object is masked from 'package:viridis':
## 
##     viridis_pal

## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.

## Run above-ground model, full model

Run below-ground model, full lui components

Use the parameters defined in the complex SEMs

get multidiv

## `set.seed(1)` was used to initialize repeatable random subsampling.

## Please record this for your records so others can reproduce.

## Try `set.seed(1); .Random.seed` for the full vector

## ...

## 50OTUs were removed because they are no longer 
## present in any sample after random subsampling

## ...

## `set.seed(1)` was used to initialize repeatable random subsampling.

## Please record this for your records so others can reproduce.

## Try `set.seed(1); .Random.seed` for the full vector

## ...

## 11288OTUs were removed because they are no longer 
## present in any sample after random subsampling

## ...

Check effect on EF MF

## [1] 496

## [1] 496

## quartz_off_screen 
##                 2

## quartz_off_screen 
##                 2

## 
##  Pearson's product-moment correlation
## 
## data:  Dim.1_all and Dim.1_fun
## t = 5.8402, df = 148, p-value = 3.184e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2928258 0.5545752
## sample estimates:
##       cor 
## 0.4327777

## Start:  AIC=35.65
## Dim.1_fun ~ Dim.1_all
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   185.24 35.654
## - Dim.1_all  1    42.691 227.93 64.762

## Start:  AIC=51.94
## Dim.1_fun ~ Dim.1_mic
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   206.48 51.938
## - Dim.1_mic  1    21.448 227.93 64.762

## Start:  AIC=58.47
## Dim.1_fun ~ Dim.1_plants
## 
##                Df Sum of Sq    RSS    AIC
## <none>                      215.68 58.472
## - Dim.1_plants  1    12.255 227.93 64.762

## Start:  AIC=53.07
## Dim.1_fun ~ multidiv
## 
##            Df Sum of Sq    RSS    AIC
## <none>                  208.05 53.073
## - multidiv  1     19.88 227.93 64.762

## Start:  AIC=53.87
## Dim.1_fun ~ LUI
## 
##        Df Sum of Sq    RSS    AIC
## <none>              209.16 53.866
## - LUI   1    18.777 227.93 64.762

##                                                Model Estimate (sd)
## 1   Functions slow-fast ~ entire community slow-fast   0.29 (0.05)
## 2             Functions slow-fast ~ plants slow-fast   0.16 (0.06)
## 3 Functions slow-fast ~ bacteria and fungi slow-fast  -0.25 (0.06)
## 4                          Functions slow-fast ~ LUI    0.35 (0.1)
## 5     Functions slow-fast ~ taxonomic multidiversity   -0.37 (0.1)
##                   Pval   R2        adj.P
## 1 3.18431138695422e-08 0.18 1.592156e-07
## 2   0.0043017352183886 0.05 4.301735e-03
## 3 0.000134527981309077 0.09 3.363200e-04
## 4 0.000369319089321567 0.08 4.616489e-04
## 5 0.000243567565734863 0.08 4.059459e-04

## lavaan 0.6-9 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         5
##                                                       
##   Number of observations                           150
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   Dim.1_fun ~                                         
##     LUI        (d)    0.132    0.087    1.511    0.131
##     Dim.1_all  (a)    0.254    0.059    4.306    0.000
##   Dim.1_all ~                                         
##     LUI        (b)    0.878    0.150    5.874    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .Dim.1_fun         1.221    0.144    8.455    0.000
##    .Dim.1_all         2.681    0.290    9.258    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     indirect          0.223    0.066    3.403    0.001
##     total             0.355    0.081    4.404    0.000
##     diff              0.091    0.132    0.691    0.490

## quartz_off_screen 
##                 2

Trait synergies

Hypotheses

Big PCA

Identification of strategy axes for each group

Plants, above- and below-ground

Bacteria & fungi

Arthropods, above-ground

Herbivores

Predators

Protists

Birds

Mites & collembola

Bats

Try to do a SEM

Run above-ground model, simple

Run below-ground model, simple

Use the parameters defined in the simple SEM

Run below-ground model, full lui components

Use the parameters defined in the complex SEMs

get multidiv

Check effect on EF MF